AI 101 - Class Recordings-Recording Class 6

Video Transcription

Video Summary

The lesson covered several key concepts in machine learning and neural networks, particularly in the context of homework. Initially, the task involved applying average pooling to a feature map, where the average of each four-square section is calculated. Following this, the dot product of an image and filter was explored, highlighting how the filter moves over the image, multiplying corresponding values and summarizing them in new values. The discourse then transitioned to managing the logistics of sharing a Google Drive link to save projects, demonstrating practical aspects of class activities. Subsequently, the lesson pivoted to understanding the machine learning training process, using a model trained on images of cats and dogs as an example. It was emphasized that models can't recognize categories outside their training data, illustrated by an attempt at classifying a non-cat/dog image. Attendees were urged to consider the impact of classifying images based on probability percentages, especially when close in value (e.g., 51% vs. 49%). Generative art, decision trees, and standard deviation were also noteworthy topics mentioned toward the transition of next lesson's subjects. Each subject had a foundational explanation to aid understanding and application in homework and classroom exercises.

Keywords

machine learning

neural networks

average pooling

dot product

Google Drive

training process

image classification

generative art

decision trees